{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c40cf31e-a94b-4469-87c8-c600902d1fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n",
      "Processing image: Data/pose.jpg\n",
      "\n",
      "0: 640x448 1 person, 107.3ms\n",
      "Speed: 7.0ms preprocess, 107.3ms inference, 8.0ms postprocess per image at shape (1, 3, 640, 448)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing video: C:\\Users\\86138\\HBPU-Machine-Learning-Course-main\\Data\\pose_vedio.mp4\n",
      "\n",
      "0: 352x640 1 person, 59.7ms\n",
      "Speed: 1.0ms preprocess, 59.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.3ms\n",
      "Speed: 1.9ms preprocess, 45.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 69.1ms\n",
      "Speed: 1.0ms preprocess, 69.1ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.9ms\n",
      "Speed: 2.0ms preprocess, 52.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.0ms\n",
      "Speed: 1.0ms preprocess, 39.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.4ms\n",
      "Speed: 2.0ms preprocess, 39.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.8ms\n",
      "Speed: 1.4ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.2ms\n",
      "Speed: 2.1ms preprocess, 50.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.6ms\n",
      "Speed: 2.0ms preprocess, 47.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.4ms\n",
      "Speed: 1.3ms preprocess, 38.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 2.4ms preprocess, 42.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.6ms\n",
      "Speed: 2.0ms preprocess, 39.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.9ms\n",
      "Speed: 2.3ms preprocess, 58.9ms inference, 1.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.8ms\n",
      "Speed: 1.0ms preprocess, 43.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.6ms\n",
      "Speed: 1.0ms preprocess, 44.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.1ms\n",
      "Speed: 1.0ms preprocess, 41.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.0ms\n",
      "Speed: 1.4ms preprocess, 39.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 1.0ms preprocess, 51.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.8ms\n",
      "Speed: 1.0ms preprocess, 45.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.6ms\n",
      "Speed: 2.0ms preprocess, 46.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.5ms\n",
      "Speed: 2.1ms preprocess, 41.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.3ms\n",
      "Speed: 2.1ms preprocess, 42.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.4ms\n",
      "Speed: 2.3ms preprocess, 52.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.2ms\n",
      "Speed: 1.0ms preprocess, 46.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.0ms\n",
      "Speed: 1.0ms preprocess, 39.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.5ms\n",
      "Speed: 1.0ms preprocess, 55.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 1.0ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.1ms\n",
      "Speed: 1.0ms preprocess, 41.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.9ms\n",
      "Speed: 2.0ms preprocess, 40.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 1.9ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 2.0ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.3ms\n",
      "Speed: 1.1ms preprocess, 38.3ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 2.0ms preprocess, 40.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.7ms\n",
      "Speed: 1.0ms preprocess, 38.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.0ms\n",
      "Speed: 2.0ms preprocess, 42.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.8ms\n",
      "Speed: 1.0ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.1ms\n",
      "Speed: 2.1ms preprocess, 37.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 1.0ms preprocess, 39.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.2ms\n",
      "Speed: 1.0ms preprocess, 37.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.8ms\n",
      "Speed: 1.0ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 2.1ms preprocess, 42.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 1.1ms preprocess, 48.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.4ms\n",
      "Speed: 1.0ms preprocess, 38.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.3ms\n",
      "Speed: 1.0ms preprocess, 47.3ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.0ms\n",
      "Speed: 1.0ms preprocess, 38.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.9ms\n",
      "Speed: 1.0ms preprocess, 46.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 2.0ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.6ms\n",
      "Speed: 2.4ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.8ms\n",
      "Speed: 1.0ms preprocess, 37.8ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 1.0ms preprocess, 45.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 35.2ms\n",
      "Speed: 2.0ms preprocess, 35.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.6ms\n",
      "Speed: 1.2ms preprocess, 37.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.8ms\n",
      "Speed: 2.2ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.0ms\n",
      "Speed: 2.1ms preprocess, 38.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.5ms\n",
      "Speed: 2.4ms preprocess, 43.5ms inference, 2.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 1.0ms preprocess, 43.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.5ms\n",
      "Speed: 2.1ms preprocess, 47.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.0ms\n",
      "Speed: 1.1ms preprocess, 37.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.3ms\n",
      "Speed: 1.0ms preprocess, 46.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.5ms\n",
      "Speed: 2.0ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 1.0ms preprocess, 42.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 36.3ms\n",
      "Speed: 1.0ms preprocess, 36.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.3ms\n",
      "Speed: 3.0ms preprocess, 51.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 1.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.0ms\n",
      "Speed: 2.1ms preprocess, 37.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.6ms\n",
      "Speed: 2.4ms preprocess, 38.6ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.5ms\n",
      "Speed: 1.0ms preprocess, 37.5ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.4ms\n",
      "Speed: 1.0ms preprocess, 37.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.3ms\n",
      "Speed: 2.1ms preprocess, 45.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.0ms\n",
      "Speed: 2.1ms preprocess, 41.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 1.3ms preprocess, 39.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.6ms\n",
      "Speed: 2.2ms preprocess, 37.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.6ms\n",
      "Speed: 1.0ms preprocess, 38.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.8ms\n",
      "Speed: 2.2ms preprocess, 50.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.6ms\n",
      "Speed: 1.0ms preprocess, 40.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.4ms\n",
      "Speed: 1.1ms preprocess, 47.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.0ms\n",
      "Speed: 1.0ms preprocess, 38.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.2ms\n",
      "Speed: 2.2ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.7ms\n",
      "Speed: 2.0ms preprocess, 39.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.8ms\n",
      "Speed: 2.1ms preprocess, 40.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.3ms\n",
      "Speed: 1.0ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.0ms\n",
      "Speed: 2.4ms preprocess, 41.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 2.0ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.6ms\n",
      "Speed: 2.2ms preprocess, 38.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 36.0ms\n",
      "Speed: 2.0ms preprocess, 36.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.3ms\n",
      "Speed: 2.0ms preprocess, 40.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.0ms\n",
      "Speed: 1.1ms preprocess, 37.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.7ms\n",
      "Speed: 1.0ms preprocess, 48.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.4ms\n",
      "Speed: 2.1ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.5ms\n",
      "Speed: 2.0ms preprocess, 40.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.7ms\n",
      "Speed: 2.2ms preprocess, 43.7ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.8ms\n",
      "Speed: 1.0ms preprocess, 62.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.9ms\n",
      "Speed: 2.0ms preprocess, 46.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.4ms\n",
      "Speed: 2.4ms preprocess, 41.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.6ms\n",
      "Speed: 1.0ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.7ms\n",
      "Speed: 1.3ms preprocess, 47.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.3ms\n",
      "Speed: 2.2ms preprocess, 57.3ms inference, 1.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 2.0ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.7ms\n",
      "Speed: 1.0ms preprocess, 50.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.9ms\n",
      "Speed: 1.0ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.7ms\n",
      "Speed: 2.1ms preprocess, 63.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.1ms\n",
      "Speed: 2.1ms preprocess, 49.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.9ms\n",
      "Speed: 1.0ms preprocess, 56.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.1ms\n",
      "Speed: 2.1ms preprocess, 55.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 2.0ms preprocess, 49.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.8ms\n",
      "Speed: 2.0ms preprocess, 44.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.8ms\n",
      "Speed: 2.0ms preprocess, 44.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.6ms\n",
      "Speed: 1.0ms preprocess, 39.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.2ms\n",
      "Speed: 1.0ms preprocess, 44.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.0ms\n",
      "Speed: 1.0ms preprocess, 52.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.0ms\n",
      "Speed: 1.1ms preprocess, 44.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.0ms\n",
      "Speed: 2.1ms preprocess, 44.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 2.0ms preprocess, 42.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 1.0ms preprocess, 42.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.4ms\n",
      "Speed: 1.0ms preprocess, 41.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 1.4ms preprocess, 43.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.5ms\n",
      "Speed: 2.2ms preprocess, 44.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.7ms\n",
      "Speed: 2.0ms preprocess, 50.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 1.0ms preprocess, 40.1ms inference, 2.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.6ms\n",
      "Speed: 1.0ms preprocess, 49.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.2ms\n",
      "Speed: 1.0ms preprocess, 43.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.7ms\n",
      "Speed: 1.0ms preprocess, 45.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.0ms\n",
      "Speed: 2.6ms preprocess, 42.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.2ms\n",
      "Speed: 2.2ms preprocess, 50.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.8ms\n",
      "Speed: 1.1ms preprocess, 41.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.3ms\n",
      "Speed: 2.0ms preprocess, 47.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.7ms\n",
      "Speed: 2.1ms preprocess, 42.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.1ms\n",
      "Speed: 2.5ms preprocess, 53.1ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.3ms\n",
      "Speed: 2.1ms preprocess, 45.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.3ms\n",
      "Speed: 1.0ms preprocess, 46.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.5ms\n",
      "Speed: 2.0ms preprocess, 44.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.7ms\n",
      "Speed: 1.0ms preprocess, 44.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 1.1ms preprocess, 47.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.4ms\n",
      "Speed: 1.2ms preprocess, 47.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 1.0ms preprocess, 45.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.6ms\n",
      "Speed: 1.0ms preprocess, 44.6ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.3ms\n",
      "Speed: 1.0ms preprocess, 53.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.6ms\n",
      "Speed: 1.0ms preprocess, 38.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.4ms\n",
      "Speed: 1.2ms preprocess, 51.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.4ms\n",
      "Speed: 1.0ms preprocess, 42.4ms inference, 1.4ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.7ms\n",
      "Speed: 1.0ms preprocess, 45.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.1ms\n",
      "Speed: 2.0ms preprocess, 53.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.9ms\n",
      "Speed: 1.2ms preprocess, 61.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.7ms\n",
      "Speed: 1.0ms preprocess, 41.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.5ms\n",
      "Speed: 1.0ms preprocess, 44.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.6ms\n",
      "Speed: 1.0ms preprocess, 44.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.8ms\n",
      "Speed: 1.0ms preprocess, 45.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 2.2ms preprocess, 40.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 1.0ms preprocess, 43.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.4ms\n",
      "Speed: 2.0ms preprocess, 44.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 2.1ms preprocess, 47.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.5ms\n",
      "Speed: 2.1ms preprocess, 53.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 2.0ms preprocess, 48.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 1.2ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.4ms\n",
      "Speed: 2.1ms preprocess, 41.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.2ms\n",
      "Speed: 2.1ms preprocess, 47.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.1ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.8ms\n",
      "Speed: 2.0ms preprocess, 38.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 2.1ms preprocess, 40.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 1.1ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.8ms\n",
      "Speed: 1.1ms preprocess, 40.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.1ms\n",
      "Speed: 1.2ms preprocess, 41.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.2ms\n",
      "Speed: 2.1ms preprocess, 39.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 2.2ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.3ms\n",
      "Speed: 1.0ms preprocess, 43.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 1.1ms preprocess, 47.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 1.3ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.7ms\n",
      "Speed: 2.1ms preprocess, 47.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.0ms\n",
      "Speed: 1.0ms preprocess, 38.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.6ms\n",
      "Speed: 2.0ms preprocess, 51.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.6ms\n",
      "Speed: 2.3ms preprocess, 39.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.4ms\n",
      "Speed: 2.5ms preprocess, 48.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.2ms\n",
      "Speed: 1.0ms preprocess, 41.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.1ms\n",
      "Speed: 1.0ms preprocess, 49.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.9ms\n",
      "Speed: 2.0ms preprocess, 43.9ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 37.1ms\n",
      "Speed: 1.4ms preprocess, 37.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.5ms\n",
      "Speed: 1.0ms preprocess, 39.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.5ms\n",
      "Speed: 1.0ms preprocess, 39.5ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.9ms\n",
      "Speed: 2.5ms preprocess, 41.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.3ms\n",
      "Speed: 2.2ms preprocess, 39.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.3ms\n",
      "Speed: 1.0ms preprocess, 46.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 1.0ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.8ms\n",
      "Speed: 2.0ms preprocess, 39.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.6ms\n",
      "Speed: 1.0ms preprocess, 38.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.4ms\n",
      "Speed: 2.3ms preprocess, 39.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.5ms\n",
      "Speed: 2.0ms preprocess, 39.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.7ms\n",
      "Speed: 2.0ms preprocess, 40.7ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.3ms\n",
      "Speed: 1.0ms preprocess, 42.3ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.4ms\n",
      "Speed: 2.1ms preprocess, 47.4ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.9ms\n",
      "Speed: 2.1ms preprocess, 43.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.7ms\n",
      "Speed: 2.0ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 1.0ms preprocess, 43.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.0ms\n",
      "Speed: 2.0ms preprocess, 46.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.9ms\n",
      "Speed: 2.2ms preprocess, 41.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.1ms\n",
      "Speed: 1.2ms preprocess, 56.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.7ms\n",
      "Speed: 1.0ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.4ms\n",
      "Speed: 2.2ms preprocess, 47.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.7ms\n",
      "Speed: 1.5ms preprocess, 43.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.4ms\n",
      "Speed: 2.0ms preprocess, 45.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.8ms\n",
      "Speed: 1.0ms preprocess, 43.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.4ms\n",
      "Speed: 2.1ms preprocess, 42.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.0ms\n",
      "Speed: 1.3ms preprocess, 48.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 1.2ms preprocess, 42.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.9ms\n",
      "Speed: 1.0ms preprocess, 40.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 1.1ms preprocess, 39.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.0ms\n",
      "Speed: 2.1ms preprocess, 39.0ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 2.3ms preprocess, 40.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.0ms\n",
      "Speed: 2.0ms preprocess, 42.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 2.0ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.0ms\n",
      "Speed: 1.0ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.8ms\n",
      "Speed: 1.1ms preprocess, 42.8ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.2ms\n",
      "Speed: 2.1ms preprocess, 46.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.6ms\n",
      "Speed: 1.3ms preprocess, 40.6ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.3ms\n",
      "Speed: 1.2ms preprocess, 47.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 38.4ms\n",
      "Speed: 2.6ms preprocess, 38.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 1.3ms preprocess, 47.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.9ms\n",
      "Speed: 2.5ms preprocess, 46.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.4ms\n",
      "Speed: 1.0ms preprocess, 46.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.3ms\n",
      "Speed: 1.0ms preprocess, 47.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 46.2ms\n",
      "Speed: 1.0ms preprocess, 46.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.9ms\n",
      "Speed: 1.1ms preprocess, 49.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.8ms\n",
      "Speed: 1.0ms preprocess, 49.8ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.1ms\n",
      "Speed: 2.1ms preprocess, 54.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.1ms\n",
      "Speed: 1.1ms preprocess, 45.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 1.0ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.7ms\n",
      "Speed: 1.3ms preprocess, 48.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.0ms\n",
      "Speed: 1.0ms preprocess, 48.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.3ms\n",
      "Speed: 2.1ms preprocess, 41.3ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.1ms\n",
      "Speed: 2.0ms preprocess, 43.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.1ms\n",
      "Speed: 1.0ms preprocess, 40.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.4ms\n",
      "Speed: 2.1ms preprocess, 42.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.9ms\n",
      "Speed: 2.0ms preprocess, 42.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 1.0ms preprocess, 45.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 41.0ms\n",
      "Speed: 2.1ms preprocess, 41.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.9ms\n",
      "Speed: 1.1ms preprocess, 48.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.7ms\n",
      "Speed: 1.0ms preprocess, 41.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 41.7ms\n",
      "Speed: 1.1ms preprocess, 41.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 59.3ms\n",
      "Speed: 1.0ms preprocess, 59.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.5ms\n",
      "Speed: 1.4ms preprocess, 50.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 56.2ms\n",
      "Speed: 1.0ms preprocess, 56.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.5ms\n",
      "Speed: 1.2ms preprocess, 48.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.6ms\n",
      "Speed: 1.1ms preprocess, 46.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.8ms\n",
      "Speed: 1.0ms preprocess, 52.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.0ms\n",
      "Speed: 2.1ms preprocess, 45.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 1.0ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.6ms\n",
      "Speed: 2.0ms preprocess, 51.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.8ms\n",
      "Speed: 2.1ms preprocess, 61.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.4ms\n",
      "Speed: 2.3ms preprocess, 46.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 1.0ms preprocess, 48.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 1.0ms preprocess, 48.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.9ms\n",
      "Speed: 2.0ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.1ms\n",
      "Speed: 2.0ms preprocess, 50.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 43.8ms\n",
      "Speed: 1.1ms preprocess, 43.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 47.6ms\n",
      "Speed: 2.0ms preprocess, 47.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 46.1ms\n",
      "Speed: 2.1ms preprocess, 46.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 2.0ms preprocess, 51.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.6ms\n",
      "Speed: 1.0ms preprocess, 43.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.2ms\n",
      "Speed: 2.0ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.3ms\n",
      "Speed: 2.3ms preprocess, 45.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.8ms\n",
      "Speed: 2.1ms preprocess, 41.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.1ms\n",
      "Speed: 1.3ms preprocess, 52.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.4ms\n",
      "Speed: 1.0ms preprocess, 41.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.7ms\n",
      "Speed: 1.0ms preprocess, 45.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.6ms\n",
      "Speed: 2.1ms preprocess, 52.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.0ms\n",
      "Speed: 2.1ms preprocess, 52.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.3ms\n",
      "Speed: 2.5ms preprocess, 51.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.3ms\n",
      "Speed: 1.0ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.4ms\n",
      "Speed: 2.3ms preprocess, 48.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 1.0ms preprocess, 42.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.9ms\n",
      "Speed: 2.4ms preprocess, 41.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.4ms\n",
      "Speed: 1.3ms preprocess, 41.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.9ms\n",
      "Speed: 2.1ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.0ms\n",
      "Speed: 2.1ms preprocess, 42.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 1.0ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.7ms\n",
      "Speed: 1.0ms preprocess, 45.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 2.3ms preprocess, 43.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.0ms\n",
      "Speed: 1.0ms preprocess, 43.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.3ms\n",
      "Speed: 1.5ms preprocess, 48.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 2.3ms preprocess, 45.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.0ms\n",
      "Speed: 1.3ms preprocess, 44.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.7ms\n",
      "Speed: 2.1ms preprocess, 46.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.1ms\n",
      "Speed: 2.2ms preprocess, 42.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.0ms\n",
      "Speed: 2.1ms preprocess, 46.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.8ms\n",
      "Speed: 2.1ms preprocess, 52.8ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.5ms\n",
      "Speed: 2.1ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.4ms\n",
      "Speed: 2.1ms preprocess, 43.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.0ms\n",
      "Speed: 2.2ms preprocess, 45.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.2ms\n",
      "Speed: 1.0ms preprocess, 44.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.1ms\n",
      "Speed: 1.2ms preprocess, 46.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.7ms\n",
      "Speed: 2.0ms preprocess, 46.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 1.3ms preprocess, 42.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 2.3ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.7ms\n",
      "Speed: 2.0ms preprocess, 51.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.8ms\n",
      "Speed: 2.2ms preprocess, 45.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.0ms\n",
      "Speed: 1.0ms preprocess, 46.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.1ms\n",
      "Speed: 2.2ms preprocess, 44.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.2ms\n",
      "Speed: 1.0ms preprocess, 49.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.4ms\n",
      "Speed: 1.2ms preprocess, 42.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.4ms\n",
      "Speed: 1.0ms preprocess, 44.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.7ms\n",
      "Speed: 2.2ms preprocess, 46.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.0ms\n",
      "Speed: 2.0ms preprocess, 46.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 1.2ms preprocess, 45.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.8ms\n",
      "Speed: 1.0ms preprocess, 57.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.7ms\n",
      "Speed: 2.3ms preprocess, 46.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 2.1ms preprocess, 48.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.7ms\n",
      "Speed: 3.1ms preprocess, 41.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.0ms\n",
      "Speed: 1.0ms preprocess, 45.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 42.2ms\n",
      "Speed: 2.4ms preprocess, 42.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 2.5ms preprocess, 50.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 2.2ms preprocess, 48.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.6ms\n",
      "Speed: 1.4ms preprocess, 47.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.9ms\n",
      "Speed: 2.0ms preprocess, 54.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.5ms\n",
      "Speed: 2.0ms preprocess, 51.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.1ms\n",
      "Speed: 2.0ms preprocess, 43.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.5ms\n",
      "Speed: 1.3ms preprocess, 47.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.0ms\n",
      "Speed: 1.4ms preprocess, 43.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.7ms\n",
      "Speed: 2.4ms preprocess, 47.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.3ms\n",
      "Speed: 1.2ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 2.0ms preprocess, 51.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 2.0ms preprocess, 47.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 45.8ms\n",
      "Speed: 2.0ms preprocess, 45.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.3ms\n",
      "Speed: 2.1ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.6ms\n",
      "Speed: 2.1ms preprocess, 41.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.5ms\n",
      "Speed: 2.4ms preprocess, 40.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.0ms\n",
      "Speed: 2.0ms preprocess, 40.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.3ms\n",
      "Speed: 2.5ms preprocess, 39.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.5ms\n",
      "Speed: 2.3ms preprocess, 45.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.6ms\n",
      "Speed: 2.1ms preprocess, 41.6ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 41.0ms\n",
      "Speed: 2.0ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 39.1ms\n",
      "Speed: 2.1ms preprocess, 39.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 40.2ms\n",
      "Speed: 2.0ms preprocess, 40.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 43.5ms\n",
      "Speed: 2.1ms preprocess, 43.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 42.7ms\n",
      "Speed: 1.1ms preprocess, 42.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 46.3ms\n",
      "Speed: 1.0ms preprocess, 46.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 44.3ms\n",
      "Speed: 2.2ms preprocess, 44.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.7ms\n",
      "Speed: 2.2ms preprocess, 50.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 82.7ms\n",
      "Speed: 3.4ms preprocess, 82.7ms inference, 2.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.4ms\n",
      "Speed: 1.0ms preprocess, 57.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.0ms\n",
      "Speed: 2.2ms preprocess, 52.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.0ms\n",
      "Speed: 2.4ms preprocess, 48.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.1ms\n",
      "Speed: 2.0ms preprocess, 49.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 47.0ms\n",
      "Speed: 2.1ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 46.5ms\n",
      "Speed: 2.1ms preprocess, 46.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.4ms\n",
      "Speed: 2.1ms preprocess, 49.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 46.1ms\n",
      "Speed: 1.5ms preprocess, 46.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.3ms\n",
      "Speed: 2.0ms preprocess, 48.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 58.5ms\n",
      "Speed: 2.4ms preprocess, 58.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 58.8ms\n",
      "Speed: 2.0ms preprocess, 58.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 3 persons, 55.5ms\n",
      "Speed: 2.0ms preprocess, 55.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 3 persons, 58.9ms\n",
      "Speed: 2.0ms preprocess, 58.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 55.8ms\n",
      "Speed: 2.0ms preprocess, 55.8ms inference, 1.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 56.0ms\n",
      "Speed: 2.3ms preprocess, 56.0ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 58.2ms\n",
      "Speed: 2.1ms preprocess, 58.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.4ms\n",
      "Speed: 2.0ms preprocess, 53.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 54.6ms\n",
      "Speed: 2.4ms preprocess, 54.6ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.5ms\n",
      "Speed: 2.3ms preprocess, 48.5ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 2.4ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.1ms\n",
      "Speed: 2.2ms preprocess, 55.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.4ms\n",
      "Speed: 1.0ms preprocess, 48.4ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.2ms\n",
      "Speed: 2.1ms preprocess, 52.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 58.4ms\n",
      "Speed: 3.0ms preprocess, 58.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.8ms\n",
      "Speed: 1.0ms preprocess, 51.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.3ms\n",
      "Speed: 2.5ms preprocess, 50.3ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.2ms\n",
      "Speed: 1.0ms preprocess, 45.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.9ms\n",
      "Speed: 2.0ms preprocess, 47.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 2.1ms preprocess, 50.9ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.9ms\n",
      "Speed: 2.6ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.2ms\n",
      "Speed: 1.0ms preprocess, 56.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.8ms\n",
      "Speed: 1.2ms preprocess, 55.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.6ms\n",
      "Speed: 2.1ms preprocess, 48.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.7ms\n",
      "Speed: 1.0ms preprocess, 62.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.8ms\n",
      "Speed: 2.0ms preprocess, 61.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.1ms\n",
      "Speed: 2.0ms preprocess, 57.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 69.2ms\n",
      "Speed: 1.0ms preprocess, 69.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.3ms\n",
      "Speed: 2.0ms preprocess, 59.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.0ms\n",
      "Speed: 3.0ms preprocess, 64.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.2ms\n",
      "Speed: 2.0ms preprocess, 64.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.8ms\n",
      "Speed: 2.0ms preprocess, 64.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.7ms\n",
      "Speed: 2.0ms preprocess, 57.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 63.1ms\n",
      "Speed: 2.2ms preprocess, 63.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.3ms\n",
      "Speed: 2.4ms preprocess, 57.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.6ms\n",
      "Speed: 2.1ms preprocess, 59.6ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 108.7ms\n",
      "Speed: 4.0ms preprocess, 108.7ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.7ms\n",
      "Speed: 2.0ms preprocess, 62.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 67.3ms\n",
      "Speed: 2.0ms preprocess, 67.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.7ms\n",
      "Speed: 2.0ms preprocess, 59.7ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.3ms\n",
      "Speed: 2.5ms preprocess, 59.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.1ms\n",
      "Speed: 2.0ms preprocess, 63.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 59.8ms\n",
      "Speed: 2.0ms preprocess, 59.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 55.0ms\n",
      "Speed: 1.4ms preprocess, 55.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.2ms preprocess, 52.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 55.4ms\n",
      "Speed: 1.4ms preprocess, 55.4ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 3 persons, 50.9ms\n",
      "Speed: 2.4ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 3 persons, 54.3ms\n",
      "Speed: 2.4ms preprocess, 54.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 46.4ms\n",
      "Speed: 2.1ms preprocess, 46.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 51.3ms\n",
      "Speed: 2.3ms preprocess, 51.3ms inference, 1.4ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.7ms\n",
      "Speed: 2.0ms preprocess, 48.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 4 persons, 54.8ms\n",
      "Speed: 2.1ms preprocess, 54.8ms inference, 1.2ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.3ms\n",
      "Speed: 2.5ms preprocess, 50.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.7ms\n",
      "Speed: 2.1ms preprocess, 49.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 59.0ms\n",
      "Speed: 2.3ms preprocess, 59.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.0ms\n",
      "Speed: 1.0ms preprocess, 48.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 51.9ms\n",
      "Speed: 1.2ms preprocess, 51.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 66.0ms\n",
      "Speed: 3.0ms preprocess, 66.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 60.6ms\n",
      "Speed: 2.3ms preprocess, 60.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.9ms\n",
      "Speed: 2.1ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 53.4ms\n",
      "Speed: 3.2ms preprocess, 53.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 53.1ms\n",
      "Speed: 3.0ms preprocess, 53.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.0ms\n",
      "Speed: 2.5ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 47.1ms\n",
      "Speed: 2.0ms preprocess, 47.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.6ms\n",
      "Speed: 2.1ms preprocess, 50.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 54.9ms\n",
      "Speed: 2.0ms preprocess, 54.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 53.2ms\n",
      "Speed: 1.9ms preprocess, 53.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.1ms\n",
      "Speed: 2.1ms preprocess, 52.1ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 54.1ms\n",
      "Speed: 2.0ms preprocess, 54.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 51.3ms\n",
      "Speed: 1.9ms preprocess, 51.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 51.9ms\n",
      "Speed: 2.1ms preprocess, 51.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.2ms\n",
      "Speed: 2.1ms preprocess, 52.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.5ms\n",
      "Speed: 2.0ms preprocess, 48.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 51.0ms\n",
      "Speed: 2.0ms preprocess, 51.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 3 persons, 60.3ms\n",
      "Speed: 3.0ms preprocess, 60.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 53.0ms\n",
      "Speed: 2.0ms preprocess, 53.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 63.5ms\n",
      "Speed: 2.3ms preprocess, 63.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 59.0ms\n",
      "Speed: 3.0ms preprocess, 59.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.0ms\n",
      "Speed: 2.1ms preprocess, 57.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 54.4ms\n",
      "Speed: 2.3ms preprocess, 54.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 64.0ms\n",
      "Speed: 2.0ms preprocess, 64.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.5ms\n",
      "Speed: 2.9ms preprocess, 50.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.9ms\n",
      "Speed: 1.7ms preprocess, 49.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 56.0ms\n",
      "Speed: 1.9ms preprocess, 56.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.1ms\n",
      "Speed: 2.0ms preprocess, 48.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.4ms\n",
      "Speed: 2.0ms preprocess, 47.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.0ms\n",
      "Speed: 2.6ms preprocess, 45.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 45.9ms\n",
      "Speed: 2.0ms preprocess, 45.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 2.0ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.6ms\n",
      "Speed: 3.0ms preprocess, 51.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.7ms\n",
      "Speed: 4.0ms preprocess, 55.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.2ms\n",
      "Speed: 2.0ms preprocess, 53.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 2.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.0ms\n",
      "Speed: 2.0ms preprocess, 59.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 94.4ms\n",
      "Speed: 7.0ms preprocess, 94.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.3ms\n",
      "Speed: 3.0ms preprocess, 55.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.8ms\n",
      "Speed: 3.0ms preprocess, 54.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 2.0ms preprocess, 51.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.0ms\n",
      "Speed: 2.0ms preprocess, 48.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 1.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.0ms\n",
      "Speed: 2.0ms preprocess, 48.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.0ms\n",
      "Speed: 2.0ms preprocess, 55.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 2.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.2ms\n",
      "Speed: 2.0ms preprocess, 49.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.0ms\n",
      "Speed: 2.0ms preprocess, 53.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 1.9ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.5ms\n",
      "Speed: 2.0ms preprocess, 51.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.1ms\n",
      "Speed: 2.0ms preprocess, 48.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.2ms\n",
      "Speed: 2.0ms preprocess, 50.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 1.0ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.9ms\n",
      "Speed: 2.1ms preprocess, 47.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.0ms\n",
      "Speed: 2.9ms preprocess, 47.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 92.7ms\n",
      "Speed: 2.0ms preprocess, 92.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 56.9ms\n",
      "Speed: 1.1ms preprocess, 56.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 1.1ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.0ms\n",
      "Speed: 2.9ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 45.1ms\n",
      "Speed: 2.0ms preprocess, 45.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.0ms\n",
      "Speed: 2.0ms preprocess, 48.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 47.5ms\n",
      "Speed: 2.0ms preprocess, 47.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 60.9ms\n",
      "Speed: 2.0ms preprocess, 60.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 52.6ms\n",
      "Speed: 2.0ms preprocess, 52.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 61.3ms\n",
      "Speed: 2.0ms preprocess, 61.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 55.0ms\n",
      "Speed: 2.0ms preprocess, 55.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 53.2ms\n",
      "Speed: 1.9ms preprocess, 53.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 56.5ms\n",
      "Speed: 2.0ms preprocess, 56.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.4ms\n",
      "Speed: 2.0ms preprocess, 50.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 50.0ms\n",
      "Speed: 2.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 48.9ms\n",
      "Speed: 2.0ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 1.1ms preprocess, 47.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.0ms\n",
      "Speed: 2.0ms preprocess, 64.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.2ms\n",
      "Speed: 2.0ms preprocess, 50.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 1.9ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 3.0ms preprocess, 50.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.1ms\n",
      "Speed: 2.0ms preprocess, 48.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.5ms\n",
      "Speed: 1.1ms preprocess, 55.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 47.0ms\n",
      "Speed: 2.0ms preprocess, 47.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.0ms\n",
      "Speed: 2.0ms preprocess, 52.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.7ms\n",
      "Speed: 1.0ms preprocess, 52.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.1ms\n",
      "Speed: 1.0ms preprocess, 50.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.2ms\n",
      "Speed: 2.0ms preprocess, 58.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.1ms\n",
      "Speed: 2.0ms preprocess, 60.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.1ms\n",
      "Speed: 3.0ms preprocess, 59.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.2ms\n",
      "Speed: 1.0ms preprocess, 56.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.0ms\n",
      "Speed: 2.0ms preprocess, 54.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.1ms\n",
      "Speed: 1.1ms preprocess, 55.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.1ms\n",
      "Speed: 2.0ms preprocess, 61.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.2ms\n",
      "Speed: 2.0ms preprocess, 61.2ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 69.4ms\n",
      "Speed: 2.0ms preprocess, 69.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 71.1ms\n",
      "Speed: 1.2ms preprocess, 71.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 70.0ms\n",
      "Speed: 2.1ms preprocess, 70.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.5ms\n",
      "Speed: 2.9ms preprocess, 66.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 68.0ms\n",
      "Speed: 2.9ms preprocess, 68.0ms inference, 1.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.4ms\n",
      "Speed: 3.0ms preprocess, 66.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.0ms\n",
      "Speed: 2.0ms preprocess, 63.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.5ms\n",
      "Speed: 1.9ms preprocess, 56.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.0ms\n",
      "Speed: 2.0ms preprocess, 60.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.2ms\n",
      "Speed: 2.0ms preprocess, 61.2ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.8ms\n",
      "Speed: 2.1ms preprocess, 58.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.8ms\n",
      "Speed: 2.0ms preprocess, 60.8ms inference, 1.5ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.7ms\n",
      "Speed: 2.0ms preprocess, 50.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.5ms\n",
      "Speed: 2.0ms preprocess, 51.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.9ms\n",
      "Speed: 2.0ms preprocess, 57.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.3ms\n",
      "Speed: 1.9ms preprocess, 53.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.0ms\n",
      "Speed: 2.0ms preprocess, 60.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.2ms\n",
      "Speed: 2.0ms preprocess, 58.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.7ms\n",
      "Speed: 1.0ms preprocess, 55.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 1.1ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.0ms\n",
      "Speed: 2.9ms preprocess, 56.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.9ms\n",
      "Speed: 2.1ms preprocess, 52.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 2.0ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 1.1ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.3ms\n",
      "Speed: 1.0ms preprocess, 55.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.4ms\n",
      "Speed: 1.9ms preprocess, 49.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.4ms\n",
      "Speed: 1.0ms preprocess, 52.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.3ms\n",
      "Speed: 2.0ms preprocess, 51.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.0ms\n",
      "Speed: 1.0ms preprocess, 52.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.4ms\n",
      "Speed: 2.0ms preprocess, 50.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.3ms\n",
      "Speed: 2.7ms preprocess, 52.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 1.1ms preprocess, 50.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.2ms\n",
      "Speed: 1.9ms preprocess, 57.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 65.2ms\n",
      "Speed: 2.1ms preprocess, 65.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.0ms\n",
      "Speed: 2.0ms preprocess, 53.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.8ms\n",
      "Speed: 2.1ms preprocess, 54.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.9ms\n",
      "Speed: 2.1ms preprocess, 56.9ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.1ms\n",
      "Speed: 2.4ms preprocess, 52.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.1ms\n",
      "Speed: 1.9ms preprocess, 51.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.9ms\n",
      "Speed: 1.1ms preprocess, 54.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.0ms\n",
      "Speed: 1.0ms preprocess, 53.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.5ms\n",
      "Speed: 2.0ms preprocess, 52.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.3ms\n",
      "Speed: 1.0ms preprocess, 53.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.1ms\n",
      "Speed: 2.0ms preprocess, 58.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.6ms\n",
      "Speed: 1.5ms preprocess, 58.6ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.8ms\n",
      "Speed: 2.1ms preprocess, 54.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.4ms\n",
      "Speed: 2.0ms preprocess, 55.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.9ms\n",
      "Speed: 2.0ms preprocess, 63.9ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.1ms\n",
      "Speed: 2.0ms preprocess, 56.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 100.7ms\n",
      "Speed: 1.6ms preprocess, 100.7ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.3ms\n",
      "Speed: 2.0ms preprocess, 58.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.9ms\n",
      "Speed: 2.0ms preprocess, 66.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 68.0ms\n",
      "Speed: 1.0ms preprocess, 68.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.2ms\n",
      "Speed: 1.3ms preprocess, 62.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 65.5ms\n",
      "Speed: 2.0ms preprocess, 65.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.0ms\n",
      "Speed: 2.0ms preprocess, 64.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.5ms\n",
      "Speed: 2.0ms preprocess, 57.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.9ms\n",
      "Speed: 2.1ms preprocess, 55.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.9ms\n",
      "Speed: 1.1ms preprocess, 55.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.3ms\n",
      "Speed: 2.0ms preprocess, 63.3ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.7ms\n",
      "Speed: 2.2ms preprocess, 58.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 99.4ms\n",
      "Speed: 2.1ms preprocess, 99.4ms inference, 1.3ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.1ms\n",
      "Speed: 1.1ms preprocess, 58.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.0ms\n",
      "Speed: 1.9ms preprocess, 53.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.1ms\n",
      "Speed: 2.0ms preprocess, 60.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.5ms\n",
      "Speed: 2.0ms preprocess, 50.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.9ms\n",
      "Speed: 1.1ms preprocess, 53.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.9ms\n",
      "Speed: 1.0ms preprocess, 49.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 47.8ms\n",
      "Speed: 1.9ms preprocess, 47.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 1.6ms preprocess, 49.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.9ms\n",
      "Speed: 1.1ms preprocess, 49.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 1.1ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.8ms\n",
      "Speed: 2.0ms preprocess, 49.8ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.4ms\n",
      "Speed: 1.0ms preprocess, 59.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 1.5ms preprocess, 49.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.9ms\n",
      "Speed: 2.1ms preprocess, 49.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 99.1ms\n",
      "Speed: 1.0ms preprocess, 99.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.9ms\n",
      "Speed: 2.0ms preprocess, 54.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.9ms\n",
      "Speed: 2.0ms preprocess, 51.9ms inference, 2.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.9ms\n",
      "Speed: 1.1ms preprocess, 56.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 1.1ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.0ms\n",
      "Speed: 2.0ms preprocess, 60.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.5ms\n",
      "Speed: 1.0ms preprocess, 53.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 1.9ms preprocess, 49.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.9ms\n",
      "Speed: 2.0ms preprocess, 53.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.0ms\n",
      "Speed: 1.0ms preprocess, 58.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.2ms\n",
      "Speed: 2.0ms preprocess, 56.2ms inference, 2.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.0ms\n",
      "Speed: 2.5ms preprocess, 66.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 74.3ms\n",
      "Speed: 4.5ms preprocess, 74.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.5ms\n",
      "Speed: 1.1ms preprocess, 63.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 63.0ms\n",
      "Speed: 2.0ms preprocess, 63.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 2.1ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.9ms\n",
      "Speed: 1.1ms preprocess, 53.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 56.1ms\n",
      "Speed: 1.1ms preprocess, 56.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.9ms\n",
      "Speed: 2.0ms preprocess, 49.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.2ms\n",
      "Speed: 2.0ms preprocess, 57.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 48.9ms\n",
      "Speed: 2.0ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.5ms\n",
      "Speed: 2.0ms preprocess, 58.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 2.5ms preprocess, 49.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.9ms\n",
      "Speed: 1.1ms preprocess, 49.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 2.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.1ms\n",
      "Speed: 2.0ms preprocess, 49.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.5ms\n",
      "Speed: 2.0ms preprocess, 57.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.3ms\n",
      "Speed: 2.3ms preprocess, 66.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.5ms\n",
      "Speed: 2.0ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.0ms\n",
      "Speed: 2.0ms preprocess, 61.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 59.2ms\n",
      "Speed: 2.0ms preprocess, 59.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.2ms\n",
      "Speed: 2.0ms preprocess, 57.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 55.5ms\n",
      "Speed: 1.1ms preprocess, 55.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 60.5ms\n",
      "Speed: 2.0ms preprocess, 60.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 62.0ms\n",
      "Speed: 2.0ms preprocess, 62.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 70.5ms\n",
      "Speed: 1.0ms preprocess, 70.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 57.9ms\n",
      "Speed: 2.1ms preprocess, 57.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 58.2ms\n",
      "Speed: 1.9ms preprocess, 58.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 65.0ms\n",
      "Speed: 2.0ms preprocess, 65.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 61.1ms\n",
      "Speed: 2.0ms preprocess, 61.1ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 65.4ms\n",
      "Speed: 2.1ms preprocess, 65.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 111.9ms\n",
      "Speed: 2.1ms preprocess, 111.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 66.5ms\n",
      "Speed: 2.0ms preprocess, 66.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 62.5ms\n",
      "Speed: 2.0ms preprocess, 62.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 60.0ms\n",
      "Speed: 2.0ms preprocess, 60.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.0ms\n",
      "Speed: 2.1ms preprocess, 59.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.6ms\n",
      "Speed: 2.0ms preprocess, 58.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.3ms\n",
      "Speed: 1.4ms preprocess, 52.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.3ms\n",
      "Speed: 2.0ms preprocess, 57.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.9ms\n",
      "Speed: 2.0ms preprocess, 55.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.2ms\n",
      "Speed: 2.0ms preprocess, 51.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.6ms\n",
      "Speed: 2.0ms preprocess, 59.6ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 1.9ms preprocess, 50.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.0ms\n",
      "Speed: 1.9ms preprocess, 55.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 64.3ms\n",
      "Speed: 1.9ms preprocess, 64.3ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 1.1ms preprocess, 50.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 80.1ms\n",
      "Speed: 3.0ms preprocess, 80.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 65.2ms\n",
      "Speed: 1.9ms preprocess, 65.2ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 61.4ms\n",
      "Speed: 2.1ms preprocess, 61.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.0ms\n",
      "Speed: 2.0ms preprocess, 54.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 65.1ms\n",
      "Speed: 2.0ms preprocess, 65.1ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.1ms\n",
      "Speed: 2.0ms preprocess, 58.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.0ms\n",
      "Speed: 2.0ms preprocess, 55.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.1ms\n",
      "Speed: 1.1ms preprocess, 51.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.4ms\n",
      "Speed: 2.0ms preprocess, 55.4ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.5ms\n",
      "Speed: 2.0ms preprocess, 55.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 70.9ms\n",
      "Speed: 1.5ms preprocess, 70.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.9ms\n",
      "Speed: 2.0ms preprocess, 59.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.9ms\n",
      "Speed: 2.1ms preprocess, 55.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.4ms\n",
      "Speed: 2.1ms preprocess, 58.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 60.9ms\n",
      "Speed: 2.0ms preprocess, 60.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.5ms\n",
      "Speed: 1.0ms preprocess, 55.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.1ms\n",
      "Speed: 2.1ms preprocess, 58.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 1.9ms preprocess, 51.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.0ms\n",
      "Speed: 2.0ms preprocess, 54.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.7ms\n",
      "Speed: 2.0ms preprocess, 54.7ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 2 persons, 49.5ms\n",
      "Speed: 1.9ms preprocess, 49.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.9ms\n",
      "Speed: 2.0ms preprocess, 55.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 96.7ms\n",
      "Speed: 2.0ms preprocess, 96.7ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 58.3ms\n",
      "Speed: 1.0ms preprocess, 58.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.5ms\n",
      "Speed: 2.0ms preprocess, 62.5ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.2ms\n",
      "Speed: 2.0ms preprocess, 59.2ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 59.0ms\n",
      "Speed: 2.0ms preprocess, 59.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.4ms\n",
      "Speed: 1.0ms preprocess, 55.4ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 62.5ms\n",
      "Speed: 2.0ms preprocess, 62.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 55.0ms\n",
      "Speed: 1.9ms preprocess, 55.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.1ms\n",
      "Speed: 1.1ms preprocess, 54.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.1ms\n",
      "Speed: 2.0ms preprocess, 51.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 53.3ms\n",
      "Speed: 1.0ms preprocess, 53.3ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 49.0ms\n",
      "Speed: 2.0ms preprocess, 49.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 2.0ms preprocess, 51.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 96.8ms\n",
      "Speed: 2.0ms preprocess, 96.8ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.0ms\n",
      "Speed: 2.0ms preprocess, 54.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.1ms\n",
      "Speed: 2.0ms preprocess, 54.1ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.5ms\n",
      "Speed: 2.0ms preprocess, 51.5ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 52.0ms\n",
      "Speed: 1.1ms preprocess, 52.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 57.0ms\n",
      "Speed: 2.0ms preprocess, 57.0ms inference, 1.1ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.0ms\n",
      "Speed: 2.0ms preprocess, 50.0ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 54.9ms\n",
      "Speed: 2.0ms preprocess, 54.9ms inference, 1.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 51.0ms\n",
      "Speed: 1.9ms preprocess, 51.0ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "\n",
      "0: 352x640 1 person, 50.9ms\n",
      "Speed: 1.1ms preprocess, 50.9ms inference, 0.0ms postprocess per image at shape (1, 3, 352, 640)\n",
      "Pose video saved to Data/output_pose_video.mp4\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import torch\n",
    "from ultralytics import YOLO\n",
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import os\n",
    "\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# 加载预训练的YOLOv8模型和姿态估计模型\n",
    "model = YOLO('Data/yolov8n.pt')  # Load the local pretrained YOLOv8 nano model from models directory\n",
    "pose_model = YOLO('Data/yolov8n-pose.pt')  # Load the pretrained YOLOv8 pose estimation model\n",
    "\n",
    "# 1. 姿态估计：对单张图片进行处理\n",
    "def process_image(image_path):\n",
    "    # 加载图片\n",
    "    image = Image.open(image_path)  # 使用PIL加载图片\n",
    "    print(f\"Processing image: {image_path}\")\n",
    "\n",
    "    # 使用姿态估计模型对图片进行处理\n",
    "    results = pose_model(image)\n",
    "\n",
    "    # results是一个列表，包含了模型的输出，我们可以从中取出渲染的图像\n",
    "    result_image = results[0].plot()  # 获取渲染后的图像\n",
    "\n",
    "    # 显示渲染后的图像\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.imshow(result_image)\n",
    "    plt.axis('off')  # 不显示坐标轴\n",
    "    plt.show()\n",
    "\n",
    "# 2. 姿态追踪：对视频进行逐帧处理并保存到新文件\n",
    "def process_video(video_path):\n",
    "    # 打开视频文件\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    print(f\"Processing video: {video_path}\")\n",
    "\n",
    "    # 检查视频是否成功打开\n",
    "    if not cap.isOpened():\n",
    "        print(\"Error: Cannot open video file!\")\n",
    "        return\n",
    "\n",
    "    # 设置输出视频\n",
    "    output_path = 'Data/output_pose_video.mp4'\n",
    "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 设置编码格式\n",
    "    fps = cap.get(cv2.CAP_PROP_FPS)  # 获取原视频的帧率\n",
    "    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n",
    "\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        \n",
    "        # 将BGR帧转换为RGB，因为YOLO模型使用RGB图像\n",
    "        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        frame_pil = Image.fromarray(frame_rgb)\n",
    "        \n",
    "        # 使用姿态估计模型对每一帧进行姿态检测\n",
    "        results = pose_model(frame_pil)\n",
    "        \n",
    "        # 在视频帧上渲染姿态关键点\n",
    "        frame_with_pose = results[0].plot()  # 获取渲染后的帧\n",
    "        \n",
    "        # 将RGB帧转换回BGR格式，适合OpenCV写入视频\n",
    "        frame_with_pose_bgr = cv2.cvtColor(frame_with_pose, cv2.COLOR_RGB2BGR)\n",
    "        out.write(frame_with_pose_bgr)  # 写入输出视频文件\n",
    "\n",
    "    # 释放资源\n",
    "    cap.release()\n",
    "    out.release()\n",
    "    print(f\"Pose video saved to {output_path}\")\n",
    "    \n",
    "\n",
    "# 主程序部分\n",
    "if __name__ == \"__main__\":\n",
    "    # 1. 对图片进行姿态估计\n",
    "    image_path = 'Data/pose.jpg'  # 使用Data/pose.jpg作为图片路径\n",
    "    process_image(image_path)\n",
    "    \n",
    "    # 2. 对视频进行姿态追踪并保存\n",
    "    video_path = r'C:\\Users\\86138\\HBPU-Machine-Learning-Course-main\\Data\\pose_vedio.mp4'  # 修改为你的视频路径\n",
    "    process_video(video_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94a57727-fd59-47f8-adbc-d7d701d3ddc4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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